UK supermarket chain Tesco has always had a strong play in data analytics. It was one of the first supermarkets to recognise the importance of customer traction through the use of data and loyalty cards, after it bought a 53% stake in analytics company Dunnhumby in 2001 (a company that has significantly increased in value since then).

Speaking at Adobe Summit in London last week, Tesco’s optimisation manager Ashish Umre took to the stage to explain to delegates that the ‘art’ of personalisation and optimisation by being inquisitive with data, is something that the organisation is striving to adopt across all departments.

However, he also warned that companies that are using data extensively to gain insights into customer behaviour and company performance need to be wary of doing this in silos and introducing their own bias into the equation.

Umre said:

I run a practice team that works horizontally across the whole organisation. A number of optimisation managers sit within different business units – Tesco grocery, Tesco bank, Tesco mobile etc. Each business unit has its own challenge, so embedding optimisation and building an analytics capability there is crucial. But at the same time, as a business, we need to look at how customers move from one business unit to another, and to understand how can we optimise and create better experiences as we understand them better.

Everyone can do data science to one degree or another. There are tools out there and there is a lot of knowledge out there that is available for free. And I think it’s the inquisitiveness that gets people to that next step.

Survival versus flexibility

Umre said that organisations need to stop thinking about data science as an abstract concept and understand it within the context of how humans process information and understand things. He described the process of data science as “a bit of an art” and a “creative process”, which requires people that can understand and analyse business problems with an inquisitive nature, where they go out and search for data. He said:

If you imagine yourselves not as data scientists, not as marketeers, just as people. Every day we process a lot of data. Going into neuroscience, we select and de-select the sensory inputs we get every day. If you’re reading something, walking around, speaking to people, your brain is making those decisions around what to select, what to de-select. And form opinions. Some of them become your belief system, some of them become your opinion until some other knowledge comes in and changes them.

Umre added that his view is that data science fits well with the concept of the stability plasticity paradox, which says your brain is both an organism that is trying you keep you stable and alive, but is also one that goes on a journey of learning, expanding its horizons and evolving over time. He said:

If you imagine and translate this into organisational design, you can imagine an organisation is trying to move in its market, remaining stable and as a working function. But at the same time we are in a business of sense and respond, we are sensing the environment and responding to create experiences that our customers and people want. Therefore it’s that plastic nature, that adaptability and evolvability of our processes, that allows us to do that.

I would say that we are all data scientists and the skills and those levels are just a means to an end. Some of this is finding data we don’t know exists and creating actions from that data or insights that we generate. How can we create compelling data products? Products that use a lot of data and create incredible experiences for our customers? And data storytelling is an important part of that. It is a multi-disciplinary area.

Umre said that as more of Tesco’s business shifts to online, this has inevitably led to more data flowing into the organisation and has helped the company understand its customers better across all channels, including in-store.

There is a lot of retail analytics and optimisation that we do at Tesco. [It has helped us to understand how to] operationalise and optimise parts of the supply chain, price optimisation, store arranging, shelf allocation. There is a lot of really interesting data science or analysis that goes behind that, to make things look really good.

It’s a great opportunity for us to try and use the data we have to try and give them a more consistent cross-channel and anytime experience that customers want.

The challenges

However, Umre was also keen to acknowledge that becoming a data organisation isn’t a simple task. Tesco is constantly having to think about how it can better put data at the centre of what it does, to help better serve the customer. And this comes with some challenges.

First of all, Umre said that creating a culture of test and learn has been essential to Tesco. And that this has had to be coupled with creating an environment of collaboration, as he didn’t want pockets of excellence within the organisation that didn’t benefit the wider company. He said:

So serving the customer is better and by understanding them better with the data we have is crucial. That data can come from a lot of different places, it’s not just customer data, it’s data that’s coming from everywhere that helps us make those decisions. Underpinning a lot of that for me – which is crucial – is a culture of test and learn. Test and learn across everything, not just digital. Every change we make is an experiment; personally, professionally, anywhere in our life.

We need to measure whether that change is moving that needle and is having an impact. Are we able to successfully measure that? In a big organisation, the only way we can do that is through collaboration. We can’t exist in silos and optimise business. You could probably optimise siloed businesses, but given the size and scale of the business, you can’t create those compelling experiences if the data is not joined up and if there is no collaboration.

So changing the culture and embedding the culture of test and learn, along with experiments, and collaboration, helps us move forward. I’m not saying that this is a done deal, not something that’s pervasive and implementing and working fantastically, it’s a journey.

As well as being strict with things like data privacy, data sharing and ensuring that customer’s identities are protected, Umre also highlighted that data quality is critically important, as well as the need to remove personal influence from the analysis. Bias can be a problem. He said:

At the same time, the kind of data we are collecting, we need to ensure that the decisions we are making with that data, we can stand firm with that data and say that it is of good quality. We can actually trace the data back to the source and reproduce where the data has been collected from. And we can replicate some of the recipes and backtrack back to the source, so that by doing so our analysis is firm and sound.

And be mindful that as you do analysis, you are not trying to push your own bias into saying ‘I think this why this is happening, I’m finding a lot of correlations’. It’s quite easy to do that. It’s a very reactive thing to do, but it’s also important to be mindful that there might be hidden dependencies that might be affecting your analysis of data, which you might not have thought of. This is where you apply the scientific method and the rigour.